• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.53-62
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• 2020

Product recommendation services that have been researched recently are only recommended through the customer's product purchase history. In this paper, we propose the digital signage service through customers' behavior pattern analysis that is recommending through not only purchase history, but also behavior pattern that customers take when choosing products. This service analyzes customer behavior patterns and extracts interests about products that are of practical interest. The service is learning extracted interest rate and customers' purchase history through the Wide & Deep model. Based on this learning method, the sparse vector of other products is predicted through the MF(Matrix Factorization). After derive the ranking of predicted product interest rate, this service uses the indoor signage that can interact with customers to expose the suitable advertisements. Through this proposed service, not only online, but also in an offline environment, it would be possible to grasp customers' interest information. Also, it will create a satisfactory purchasing environment by providing suitable advertisements to customers, not advertisements that advertisers randomly expose.

• Journal of Korean Society for Quality Management
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• v.49 no.1
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• pp.81-95
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• 2021

Purpose: The purpose of this study was to analysis pattern of nonconforming farmers who is one of the factors of unconformity in residual pesticides. Methods: Pattern analysis of nonconforming farmers were analyzed through convergence of safety data and farmer's DB data. Exploratory data analysis and association rule analysis were used for extracting factors related to unconformity. Results: The results of this study are as follows; regarding the exploratory data analysis, it was found that factors of farmers influencing unconformity in residual pesticides by total 9 factors; sampling time, gender, age, cultivation region, farming career, agricultural start form, type of agriculture, cultivation area, classification of agricultural products. Regarding the association rule analysis, non-conformity association rules were found over the past three years. There was a difference in the pattern of nonconforming farmers depending on the cultivation period. Conclusion: Exploratory data analysis and association rule analysis will be useful tools to establish more efficient and economical safety management plan for agricultural products.

• Health Policy and Management
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• v.2 no.1
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• pp.147-166
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• 1992

The purpose of this study is to analyze the effects of the health center hospital on the health service utilization pattern of the rural population in a county. Two field studies had been conducted in Yonchon County, Kyunggi Province, on February 1989 and on August 1991 before and after the establishment of the Yonchon health center hospital. This study revealed that Yonchon health center hospital occupied 7.3% of total outpatient visits and 16.8% of hospitalization of the county population and the self-sufficient rate of the outpatient visit and hospitalization of Yonchon County between two field studies increased by 1.7% and 20.9% each. Yonchon health center hospital contributed to the growth of the public health sector but it weakened the role of health sub-centers. For the efficient health service utilization of the population in that County, more investment to health center hospital would be needed and the primary health activities of the health subcenter should be enforced.

• Journal of Information Science Theory and Practice
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• v.2 no.2
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• pp.33-47
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• 2014

Mobile devices are the most important equipment for accessing various kinds of services. These services are accessed using wireless signals, the same used for mobile calls. Today mobile services provide a fast and excellent way to access all kinds of information via mobile phones. Mobile service providers are interested to know the access behavior pattern of the users from different locations at different timings. In this paper, we have introduced an associated tree for analyzing user behavior patterns while moving from one location to another. We have used four different parameters, namely user, location, dwell time, and services. These parameters provide stronger frequent accessing patterns by matching joins. These generated patterns are valuable for improving web services, recommending new services, and predicting useful services for individuals or groups of users. In addition, an experimental evaluation has been conducted on simulated data. Finally, performance of the proposed approach has been measured in terms of efficiency and scalability. The proposed approach produces excellent results.

• Journal of the Economic Geographical Society of Korea
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• v.1 no.1
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• pp.33-56
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• 1998

This study examines the characteristics of service economies and their impacts on the spatial transformation of Korea during the last IS years. This study reviews the different perspectives for the tertiarization Process. It focuses on the spatial variation in the growth and location of Producer service industries. Based on the analyses of industrial and occupational compositions, services. particularly producer services, have played a major role in creating new job opportunities since the late 1980s. The ratio of services to merchandise trade is approximately 1:4, but service trades have increased since the early 1990s. Producer service activities have grown very rapidly, and the information processing service has been over-concentrated in Seoul. Further headquarters of bank and insurance services are overwhelmingly concentrated into Seoul. The firms whose headquarters are located in Seoul have linkage Pattern on a nationwide scale. The pattern of employment growth in producer services shows a clear core-Periphery disparity. In the light of the observed pattern of regional differentiation in producer service employment, some wider implications of the distribution of producer service activities for regional economic Performance are considered.

• Journal of Digital Contents Society
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• v.15 no.5
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• pp.569-577
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• 2014

The various services are required to user in time and space. It is important to provide suitable service to user according to user's circumstance. Therefore it is need to provide services to user through mining by latest information of user activity and service history. In this paper we propose a mining method to search association rule using service history based on spatiotemporal information and service ontology. In this method, we find the associative service pattern using level-cross tree on service ontology. The proposed method is to be a basic research to find the service pattern to provide high quality service to user according to season, location and age under the same context.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.672-674
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• 2006

Information and communication technology joins in and with development the mobile service pattern which the tourist uses is various and it is changing. Jeiu area is the domestic maximum tourist resort which the annual 5 million visit. The mobile service pattern change from mobile market predition does to be possible. Research analysis against sightseeing industry which is major industry and a mobile service market it led from the research which it sees location base and the mobile fusion technology.

• Korean Journal of Veterinary Service
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• v.30 no.3
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• pp.363-374
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• 2007

A biochemical characterization and antimicrobial drugs susceptibility study was conducted in four breeding kennel which was canine abortion caused by Brucella canis in Gyeongbuk province in 2003-2006. Total of 267 dogs domesticated in the four kennel were examination. Among them, 143 (53.6%) dogs were sero-positive and 25 of blood samples were isolated to Brucella canis. At amplification of 35KDa-BCSP gene using PCR, 711 bp DNA fragment was same visible in 25 isolates and B canis RM6/66. Biochemical characterization of B canis isolated was non-hemolytic, no production of $H_2S$, no fermentation of carbohydrates, catalase-positive, oxidase-positive, indol-negative, hydrolyzation of urea, reduction of nitrate and development of thionin dye medium. Using disk-diffusion method, all of 25 strains tested were found to be highly susceptible to tetracycline, aminoglycoside, quinolone, macrolide antibiotics, rifampin and ampicillin in vitro. Using PFGE with restriction enzyme Smi I, 25 isolates tested were typed to 2 pattern, S1 and S2.

• Korean Journal of Veterinary Research
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• v.45 no.2
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• pp.163-168
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• 2005

This study was carried out to investigate the antibiotic resistance pattern of Enterococcus spp. and Staphylococcus aureus (S. aureus) isolated from chicken feces. All isolates showed high resistance to erythromycin (E) and tetracycline (TE). Of the 63 Enterococcus faecalis (E. faecalis) isolates, 73.0% were resistant to E and 98.4% to TE. Of the 44 Enterococcus faecium (E. faecium) isolates, 50.0% were resistant to E and 95.5% to TE. Of the 52 S. aureus isolates, 57.6% were resistant to E and 96.2% to TE. The prevalence of two and three drugs resistance pattern were 28.6% and 17.5% of E. faecalis, 40.9% and 25.0% of E. faecium and 38.5% and 23.1% of S. aureus, respectively. The multiple resistance pattern to six drugs was observed in 1 E. faecalis isolates, and five drugs resistance pattern were seen in 1 E. faecalis, 1 E. faecium and 1 S. aureus isolates. The prevalence of resistant organisms in Korea probably reflects lack of proper antibiotic policy resulting in prolonged and indiscriminate use of antimicrobial agents.

• The Journal of Information Systems
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• v.31 no.3
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.